1. Field of the Disclosure
The disclosure relates generally to positional tracking, and more particularly to optics-based positional tracking systems and methods for virtual reality and/or augmented reality applications with improved features and characteristics.
2. General Background
Various positional tracking systems and methods are known in the art, varying in parameters such as tracking precision, tracking volume, tracking markers, manufacturing cost, and complexity of user setup. One current generation of desktop virtual reality (“VR”) experiences are created using head-mounted displays (“HMDs”), which can be tethered to a stationary computer (such as a personal computer (“PC”), laptop, or game console), or self-contained. In addition, desktop VR experiences generally try to be fully immersive and disconnect the users' senses from their surroundings. The tracking requirements for a wide variety of applications in this technical space can be met by a system that tracks six degrees of freedom (“6DOF”) positions of multiple rigid objects (e.g., HMD, input wand, desktop geometry) in a tracking volume typically limited to approximately two cubic meters. However, to create more immersive VR experiences, the tracking system is required to be precise (e.g., down to one millimeter and one-degree precision) while maintaining very low latency (e.g., approximately one millisecond delay between action and response). In addition, it is typically desirable that the tracking technology should be relatively easy to set up and affordable to the average home user.
Certain positional tracking systems currently known in the art fully or partially rely on tracking markers attached to objects, and then track the marked objects. In such systems, a tracked object typically must be covered with large tracking markers that can encode several bits of data, such that typically only large objects can be tracked. For this reason most such systems, known as “PTAM/SLAM systems” (acronyms for Positional Tracking and Mapping for Small AR Workspaces, and Simultaneous Localization and Mapping, respectively), locate the camera on the HMD and place the tracking markers on the walls of the environment. This approach has several disadvantages, for example: it typically requires the VR user to greatly modify the appearance of his or her environment by covering all viewing directions with large tracking markers; it typically requires the user to perform a complex calibration step in order to the map the environment; and the tracking cameras attached to the HMD typically require a good lens for precise tracking, and this increases the weight of the HMD, typically significantly. Since the tracking markers are typically complex in design in such implementations, their decoding is usually performed on the PC instead of an onboard processor in or near the HMD, and this typically increases the amount of data sent from the camera to the computer and the tracking latency.
Advances in computer vision algorithms have made it possible to do away with tracking markers in limited scenarios by using natural image/scene features instead. Unfortunately, current tracking algorithms that rely on natural image features are typically not robust/precise enough to work consistently in many home environments, which often contain transparent, shiny, and/or textureless objects. Extracting and identifying natural features from images also tends to be computationally expensive.
It is desirable to address the current limitations in this art.